In addition to the input patches having spatial dimensions, they also have a time dimension in the form of multiple time slices (see Supplement REF  Table REF  for details.) This is to ensure that the network has access to the temporal dynamics in the input features.
After padding and concatenation together along the depth axis, the input sets are embedded using a convolutional recurrent network [1]} in the time dimension [2]}.
